A method for tracking and trajectory prediction of a UAV based on radar plot information

By using adaptive spatiotemporal coding and physical constraint feature extraction based on radar point information, combined with Transformer and Kalman filtering, the problem of tracking and predicting low, slow, and small targets in complex environments is solved. This achieves high-precision, robust trajectory prediction and system adaptability, making it suitable for embedded systems.

CN121959458BActive Publication Date: 2026-07-07CHONGQING UNIV +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHONGQING UNIV
Filing Date
2026-03-30
Publication Date
2026-07-07

AI Technical Summary

Technical Problem

Existing technologies for tracking and predicting the trajectory of low, slow, and small targets in the low-altitude airspace suffer from poor adaptability to complex maneuvers, fixed noise parameters, and a lack of physical consistency in deep learning methods. In particular, under low signal-to-noise ratio, high maneuverability, and sparse observation environments, it is difficult to achieve stable and accurate state estimation and trajectory prediction.

Method used

A radar-based approach is employed, employing adaptive spatiotemporal coding, physical constraint feature extraction, Transformer-based spatiotemporal hybrid prediction, and adaptive Kalman filter correction to construct a data-driven pattern learning and model-driven estimation cascade framework. By combining causal convolutional neural networks and Kalman filtering, semantic understanding of target motion patterns and embedding constraints of physical laws are achieved.

Benefits of technology

It improves tracking accuracy and robustness in complex environments, ensures the physical reliability of the trajectory and the adaptability of the system, and is suitable for embedded systems with limited computing resources, meeting the requirements of real-time performance and reliability.

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Abstract

The present application relates to the technical field of radar signal processing and automatic tracking, in particular to a UAV tracking and trajectory prediction method based on radar plot information. The method comprises: acquiring and preprocessing radar plot sequences; performing adaptive space-time coding on the sequences and extracting a physical constraint feature sequence; inputting the features into a motion pattern analysis network to obtain a motion pattern label and a pattern enhanced feature sequence; inputting the space-time coding feature sequence, the physical constraint feature sequence and the pattern enhanced feature sequence into a space-time hybrid module based on a Transformer architecture to perform acceleration increment prediction; correcting the predicted state using adaptive Kalman filtering; performing multi-step prediction based on the corrected state to generate a future trajectory. The present application combines physical guided deep learning with classical filtering technology, thereby improving the precision, physical credibility and system robustness of state estimation and trajectory prediction of low, slow and small targets in complex environments.
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Citation Information

Patent Citations

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